An important task in the Internet of Things (IoT) is field monitoring, where
multiple IoT nodes take measurements and communicate them to the base station
or the cloud for processing, inference, and analysis. This communication
becomes costly when the measurements are high-dimensional (e.g., videos or
time-series data). The IoT networks with limited bandwidth and low power
devices may not be able to support such frequent transmissions with high data
rates. To ensure communication efficiency, this article proposes to model the
measurement compression at IoT nodes and the inference at the base station or
cloud as a deep neural network (DNN). We propose a new framework where the data
to be transmitted from nodes are the intermediate outputs of a layer of the
DNN. We show how to learn the model parameters of the DNN and study the
trade-off between the communication rate and the inference accuracy. The
experimental results show that we can save approximately 96% transmissions with
only a degradation of 2.5% in inference accuracy. Our findings have the
potentiality to enable many new IoT data analysis applications generating large
amount of measurements.

本文研究了如何通过深度神经网络的测量压缩和推理，实现 IoT 数据通信的有效率，并在实验中取得了 96% 的数据传输量节省和 2.5% 推理准确度下降的成果。

物联网作为深度神经网络

The Internet of Things as a Deep Neural Network

The recently introduced compressive sensing (CS) framework enables digital
signal acquisition systems to take advantage of signal structures beyond
bandlimitedness. Indeed, the number of CS measurements required for stable
reconstruction is closer to the order of the signal complexity than the Nyquist
rate. To date, the CS theory has focused on real-valued measurements, but in
practice, measurements are mapped to bits from a finite alphabet. Moreover, in
many potential applications the total number of measurement bits is
constrained, which suggests a tradeoff between the number of measurements and
the number of bits per measurement. We study this situation in this paper and
show that there exist two distinct regimes of operation that correspond to
high/low signal-to-noise ratio (SNR). In the measurement compression (MC)
regime, a high SNR favors acquiring fewer measurements with more bits per
measurement; in the quantization compression (QC) regime, a low SNR favors
acquiring more measurements with fewer bits per measurement. A surprise from
our analysis and experiments is that in many practical applications it is
better to operate in the QC regime, even acquiring as few as 1 bit per
measurement.

本文研究了基于有限字母表编码的测量压缩和数据压缩的压缩感知框架，在高信噪比和低信噪比两种情况下，通过测量位数和每个测量数据的位数之间的权衡来实现稳定重建，并发现在许多实际应用中，以低信噪比进行量化压缩 (obtained more measurements with fewer bits per measurement) 通常更好。